(Cukur et al., Journal of Neuroscience 2013, PDF 16M) Many previous fMRI studies have claimed that the Fusiform Face Area (FFA) is highly specialized for processing face-related information, and that it does not represent other types of visual information. This paper demonstrates that FFA consists of three separate functional modules. All three modules are selective for faces, but each is also selective for other visual information as well. Furthermore, the off-face selectivity differs across the three modules, reflecting the position of each module within larger visually selective gradients reported in Huth et al. 2012.
(Stansbury et al., Neuron 2013, PDF 8.9M) Much of human visual cortex appears to be selective for specific categories of natural scenes. However, it is unknown whether this scene selectivity is essentially arbitrary, or rather whether it reflects the statistical strucure of natural scenes. This paper uses a machine learning algorithm to discover the intrinsic categorical structure of natural scenes, and then uses fMRI to show that the human brain represents these natural categories. You can also watch a video summary provided by the first author, Dustin Stansbury.
(Cukur et al., Nature Neuroscience 2013) Many computational models of the brain assume that sensory neurons function as labeled lines whose tuning does not vary. However, anatomical considerations and neurophysiological studies suggest that tuning of neurons far from the sensory periphery must change, depending on the state of visual attention. This paper presents the first fMRI evidence that attention changes tuning of single voxels across much of cerebral cortex. This effect suggests that the human brain can be dynamically reconfigured to allocate computational resources to desired tasks.
(Huth et al., Neuron 2012) Functional MRI studies over the past 20 years have revealed several visual areas that are highly selective for specific object categories. However, humans can perceive thousands of distinct object categories, so it would be impossible for the brain to represent every object category in its own functional area. This paper shows that the brain maps categories continuously across the cerebral cortex, using a low-dimensional semantic space.
(Nishimoto et al., Current Biology 2011) This paper presents the first successful approach for reconstructing passively viewed natural movies from brain activity measured by fMRI.
(Naselaris et al., Neuroimage 2010, PDF 841KB) This paper reviews the state of “brain decoding” research circa 2010, and advocates one particularly powerful approach: Bayesian decoding of voxel-wise models.
(Naselaris et al., Neuron 2009, PDF 3.7MB) This paper presents the first successful approach for reconstructing natural images from brain activity.
(Kay et al., Nature 2008, PDF 5.4MB) Functional MRI measures hemodynamic signals that are indirectly coupled to neural activity. Thus, fMRI is an inherently limited method that cannot recover all of the information available in the brain. However, this landmark paper shows that far more information can be recovered from fMRI signals than had been believed previously.
(Hansen et al., Journal of Neuroscience 2007, PDF 4.5MB) The human visual system consists of over three dozen distinct visual areas. Several early visual areas (V1, V2, V3, MT) appear to be functionally homologous between humans and non-human primates such as the macaque. However, there has been debate about one specific visual area, V4. V4 in primates appears to be organized into separate ventral and dorsal components. In contrast, fMRI studies in humans have argued that human V4 is organized differently, and that the ventral and dorsal halves of V4 are contiguous. This paper reports a systematic and detailed mapping study of area V4, including a crucial experiment on selective attention. We conclude that human V4 is organized analogously to V4 in non-human primates, with separate ventral and dorsal halves.